{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/personalized-gaussian-processes-for","title":"Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)","arxiv_id":"1802.08561","date":"2018-02-22","proceeding":null,"authors":["Yuria Utsumi","Ognjen Rudovic","Kelly Peterson","Ricardo Guerrero","Rosalind W. Picard"],"abstract":"In this paper, we introduce the use of a personalized Gaussian Process model\n(pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictor\nof Alzheimer's Disease (AD) in the cognitive domain -- using data from each\npatient's previous visits, and testing on future (held-out) data. We start by\nlearning a population-level model using multi-modal data from previously seen\npatients using a base Gaussian Process (GP) regression. The personalized GP\n(pGP) is formed by adapting the base GP sequentially over time to a new\n(target) patient using domain adaptive GPs. We extend this personalized\napproach to predict the values of ADAS-Cog13 over the future 6, 12, 18, and 24\nmonths. We compare this approach to a GP model trained only on past data of the\ntarget patients (tGP), as well as to a new approach that combines pGP with tGP.\nWe find that the new approach, combining pGP with tGP, leads to large\nimprovements in accurately forecasting future ADAS-Cog13 scores.","url_abs":"http://arxiv.org/abs/1802.08561v4","url_pdf":"http://arxiv.org/pdf/1802.08561v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"personalized-gaussian-processes-for","repo_url":"https://github.com/yuriautsumi/PersonalizedGP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}